Hello, Indrajit,
a "quick and dirty" solution could be to loop over the names of the
relevant variables of the data frame (in your case over the first three
variables) and use "[[ ]]" for indexing:
for (i in names( df)[ 1:3]) {
z <- glm( df[[ i]] ~ df$Independent1)
print( z)
}
An output that is a little bit more informative w. r. t. the actually used
model formula is obtained by
for( i in names( df)[ 1:3]) {
modelform <- formula( paste( i, "~ Independent1"))
z <- eval( substitute( lm( f, data = airquality),
list( f = modelform) ))
print( z)
}
Hth,
Gerrit
On Tue, 15 Mar 2011, Indrajit Chatterjee wrote:
> Hi,
>
> I have created a dataframe (lets call is df) that contains the following
> variables
>
> "Dependent1" "Dependent2" Dependent3"
"Independent1"
>
> I want to do the following regressions:
>
> z<- glm( df$Dependent1 ~ df$Independent1)
> z<- glm( df$Dependent2 ~ df$Independent1)
> z<- glm( df$Dependent3 ~ df$Independent1)
>
> and so on
>
> I wanted to put this in a for loop e.g.
>
> for (i in 1:3) {
>
> z<- glm( df$ColumnName_i ~ df$Independent1)
> print(z)
>
> }
>
>
> How do I specify the column name in the regression above for the dependent
> variable
>
> [[alternative HTML version deleted]]
>
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